The 29 Best And Free ChatGPT And Generative AI Courses And Resources

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Most of us are familiar with chatbots which are computer programs designed to simulate conversation with human users, especially over the Internet. But have you ever heard of ChatGPT and Generative AI? ChatGPT stands for "Chat Generative Pre-training Transformer" and it represents a breakthrough in natural language processing technology.

Generative AI, on the other hand, is an AI technique that can generate new content based on a set of given data. With ChatGPT and Generative AI, you can train a machine to understand and respond to natural language much like a human would, as well as creating new text content with a sense of creativity and originality.

With the growing demand for smarter chatbots, content creation, and virtual assistants, mastering ChatGPT and Generative AI is becoming more and more valuable. That's why we've created this list of the best and free online courses and resources for anyone interested in learning ChatGPT and Generative AI.

ChatGPT and Generative AI have a wide range of applications such as creating virtual assistants, content creation, question and answering systems, chatbots, and more. Here are a few examples of how these technologies are being used in the real world:

• A chatbot created using ChatGPT for a hotel reservation service that is able to respond to customer inquiries and make bookings.
• A content creation tool that uses Generative AI to generate unique and engaging blog articles for a marketing company.
• A virtual assistant created using ChatGPT that can carry out tasks such as setting reminders, sending emails, and making phone calls.
• A question and answering system created using Generative AI that can provide accurate responses to complex questions in various fields such as science and technology.

Top 29 Free ChatGPT and Generative AI Courses and Resources

• Natural Language Processing in TensorFlow (Coursera)

  Learn how to use TensorFlow to build natural language processing models that can be used for sentiment analysis, text classification, and text generation. This course is designed for those with no prior experience in natural language processing and TensorFlow.

• Google AI Education

  Google's AI Education offers free courses and resources for anyone interested in learning about AI and machine learning. The courses cover various topics such as natural language processing, computer vision, and deep learning.

• Natural Language Processing Nanodegree (Udacity)

  This nanodegree program is designed to provide a comprehensive understanding of natural language processing and its applications. The program covers various topics such as tokenization, part-of-speech tagging, and named entity recognition.

• Sequence Models for Time Series and Natural Language Processing (Coursera)

  This course teaches you how to use sequence models such as recurrent neural networks and transformers for time series analysis and natural language processing. The course is designed for those with some experience in TensorFlow and natural language processing.

• IBM AI Engineering Professional Certificate (edX)

  This professional certificate program is designed to provide a comprehensive understanding of AI and machine learning. The program covers various topics such as data science, natural language processing, computer vision, and deep learning.

• Natural Language Processing with Sequence Models (Coursera)

  This course teaches you how to use sequence models such as recurrent neural networks and transformers for natural language processing tasks such as text classification and language modeling. The course is designed for those with some experience in deep learning and natural language processing.

• Probabilistic Graphical Models (Coursera)

  This course teaches you how to use probabilistic graphical models for various machine learning tasks such as natural language understanding and image recognition. The course is designed for those with some experience in machine learning algorithms and statistics.

• Machine Learning Engineer Nanodegree (Udacity)

  This nanodegree program is designed to provide a comprehensive understanding of machine learning techniques and their applications. The program covers various topics such as supervised and unsupervised learning, reinforcement learning, and natural language processing.

• Collection of Free Machine Learning and Python Books (FreeCodeCamp)

  This collection provides a list of free books on machine learning, Python, and data science that are helpful for those interested in learning about AI and machine learning. The books cover various topics such as deep learning, natural language processing, and computer vision.

• Machine Learning Mastery

  Machine Learning Mastery offers free and paid courses and resources on machine learning and data science. The courses cover various topics such as deep learning, natural language processing, and computer vision. In addition, the website also provides free tutorials and guides on various machine learning topics.

• Transformers: State-of-the-Art Natural Language Processing (Hugging Face)

  Hugging Face's transformers library provides a simple and easy-to-use interface for building and training state-of-the-art natural language processing models. The library supports various transformer architectures such as GPT-2, BERT, and RoBERTa.

• Transformers Text Generation Example (GitHub)

  This GitHub repository provides an example of how to use Hugging Face's transformers library for text generation using a GPT-2 model. The repository also contains pre-trained models that can be used for text generation.

• How to Fine-Tune GPT-2 for Text Generation (Towards Data Science)

  This article provides a step-by-step guide on how to fine-tune GPT-2 for text generation using Hugging Face's transformers library. The article also provides tips and tricks for improving the quality of the generated text.

• GPT-2 by OpenAI

  OpenAI's GPT-2 is a state-of-the-art language processing AI model that can generate high-quality natural language text. The model can be fine-tuned for various natural language processing tasks such as question answering and text generation.

• Everything You Need to Know About GPT-3 (Towards Data Science)

  This article provides an overview of GPT-3, currently one of the most advanced AI language models. The article covers various features and capabilities of GPT-3, as well as some potential applications and ethical considerations.

• neuralcoref: Coreference Resolution with Neural Networks (GitHub)

  This GitHub repository provides an implementation of a neural network model for coreference resolution, which is used to find and link entities that are referred to multiple times in a text. The model can be fine-tuned for various natural language processing tasks such as text summarization and text generation.

• How to Build a Basic Chatbot with GPT-2 (Towards Data Science)

  This article provides a tutorial on how to build a simple chatbot using GPT-2 and Python. The chatbot can be fine-tuned for various conversational scenarios such as customer service and personal assistant.

• Generative Adversarial Networks with TensorFlow (GitHub)

  This GitHub repository provides an implementation of generative adversarial networks (GANs) using TensorFlow, a popular deep learning framework. GANs can be used for various generative tasks such as image generation and text generation.

• The Basics of Generative Adversarial Networks (Towards Data Science)

  This article provides an introduction to GANs, a popular generative technique in machine learning. The article covers various GAN architectures and applications, as well as some challenges and future directions.

• Introduction to Generative Adversarial Networks (Towards Data Science)

  This article provides a beginner-friendly introduction to GANs, covering their basic concepts and applications. The article also provides some tips and tricks for training GANs effectively.

• Guide to Hyperparameters Search for Deep Learning Models (FloydHub)

  This article provides a comprehensive guide to hyperparameter tuning for deep learning models, which can significantly improve their performance. The article covers various hyperparameter search methods and tools, as well as some best practices.

• The Ultimate Guide to Hyperparameter Tuning in Deep Learning (Towards Data Science)

  This article provides a step-by-step guide to hyperparameter tuning for deep learning models, covering various search strategies and evaluation metrics. The article also provides some advanced techniques such as transfer learning and ensemble methods.

• 11 Important Model Evaluation Metrics for Machine Learning Everyone should Know (Analytics Vidhya)

  This article provides an overview of various model evaluation metrics that can be used to assess the performance of machine learning models, such as accuracy, precision, recall, and F1-score. The article also provides some guidance on when to use each metric.

• Aware AI Leads to an Ethical AI (Towards Data Science)

  This article discusses the importance of ethical considerations in AI and machine learning, and how awareness and education can help organizations deploy AI systems more responsibly. The article also provides some examples of ethical AI use cases.

• 5 Hacks to Make Your AI Applications More Ethical (Data Science Central)

  This article provides some practical tips on making your AI applications more ethical, such as reducing bias, improving transparency, and involving diverse perspectives. The article also provides some examples of ethical AI tools and frameworks.

Conclusion

1. With the increasing demand for intelligent chatbots, virtual assistants, and content creation, ChatGPT and Generative AI are becoming essential skills for those interested in machine learning and AI. By mastering these technologies, you can create more intelligent and effective natural language processing models, as well as generate new and creative text content.

2. There are many free online courses and resources available for those interested in learning ChatGPT and Generative AI, including Coursera, Udacity, Google AI Education, and Machine Learning Mastery. In addition, there are also many useful tools and frameworks such as Hugging Face's transformers library and OpenAI's GPT-2 model.

3. Finally, it is important to be aware of the ethical implications of AI and machine learning, and to strive for more responsible and transparent deployment of these technologies. As AI becomes more integrated into our lives, it is crucial to ensure that it benefits society as a whole, rather than exacerbating social inequalities or biases.